mirror of
https://github.com/NVIDIA/dgx-spark-playbooks.git
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a680d0472b
Adds a Claude Code plugin structure that exposes each NVIDIA DGX Spark
playbook as a triggerable skill, with an index skill ('dgx-spark') that
routes users to the right leaf based on intent and encodes the
relationship graph between playbooks (prerequisites, alternatives,
composes-with, upgrade paths).
Structure:
- overrides/*.md hand-curated frontmatter + Related sections
- scripts/generate.mjs zero-dep Node generator: nvidia + overrides → skills
- scripts/install.sh symlinks skills into ~/.claude/skills (--plugin mode available)
- skills/ committed, browsable, installable without Node
- .github/workflows/ auto-regenerates skills/ when playbooks/overrides change
Initial curated leaves: ollama, open-webui, vllm, connect-to-your-spark.
Remaining 37 leaves use generator fallback (title + tagline + summary
extracted from README) and can be curated incrementally via overrides/.
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| name | description |
|---|---|
| dgx-spark-speculative-decoding | Learn how to set up speculative decoding for fast inference on Spark — on NVIDIA DGX Spark. Use when setting up speculative-decoding on Spark hardware. |
Speculative Decoding
Learn how to set up speculative decoding for fast inference on Spark
Speculative decoding speeds up text generation by using a small, fast model to draft several tokens ahead, then having the larger model quickly verify or adjust them. This way, the big model doesn't need to predict every token step-by-step, reducing latency while keeping output quality.
Outcome: You'll explore speculative decoding using TensorRT-LLM on NVIDIA Spark using two approaches: EAGLE-3 and Draft-Target. These examples demonstrate how to accelerate large language model inference while maintaining output quality.
Full playbook: /Users/jkneen/Documents/GitHub/dgx-spark-playbooks/nvidia/speculative-decoding/README.md